Release Overview:
New features:
We are pleased to announce enhancements to our Data Masking Service.
Advanced preferences for non-business data columns, capability to execute SQL scripts pre- and post-masking process, and greater improvements in our file masking. These features aim to boost efficiency, accuracy, and flexibility.
Masking Preferences for non-business data columns
This new feature introduces a robust set of preferences to streamline the masking process by excluding non-essential non-business data columns. This targeted approach speeds up the sensitive data scan process and safeguards the database schema and data integrity.
Key benefits:
Reduces the time required for sensitive data scans by focusing only on columns that contain primary business data.
Prevents changes to non-business data, maintaining the integrity and layout of the database schema.
Ensures that only columns with sensitive business data are subjected to masking, reducing overhead and improving performance.
Supported excluded columns details:
System-generated identifiers: automatically generated by the system, such as UUIDs or sequence numbers.
Identity columns: autoincrement columns are typically used for primary keys, which do not contain sensitive information.
View-related columns: columns associated with views and materialized views to prevent disruption of these derived structures.
Virtual columns: calculated or partitioned columns that are dynamically generated from other data within the database.
Foreign keys: foreign key columns preserving relational integrity across different tables.
System dates and timestamps: system-generated date and timestamp columns that are typically used for recording transaction times or modifications.
Supported database types:
This feature supports a wide range of database types.
Customization:
This feature is enabled by default during the initial creation of the data source and can be edited during the Sensitive Data Scan process. Users have the flexibility to override these default settings also manually within the Masking Editor.
SQL script execution as part of the Masking process
Customers can now automate the execution of SQL scripts to run immediately before or after the data masking process. This feature ensures that any necessary database operations can be performed exactly when needed, enhancing the automation and customization of the data masking workflow.
Key Benefits:
Workflow integration: Seamlessly integrates SQL scripts into the masking workflow, allowing for automated data preparation or cleanup.
Error Handling: Enhances reliability as the masking process will fail if the SQL scripts do not execute correctly, ensuring issues are addressed promptly.
Admin control: Only admins can set up these scripts, ensuring that script execution remains secure and controlled.
File data source enhancements
Enhancements to the file type masking are designed to increase flexibility, efficiency, and effectiveness in managing file-based data sourcese. By allowing more detailed file structure customization, supporting local files, and enabling zipped file processing, we provide our users with powerful tools to better meet their data masking needs.
File data source schema customization
Users now can edit and customize the file structure of data sources in CSV and fixed-size formats. This new feature facilitates greater control over data handling and integration by allowing modifications to the file structure post-creation.
Support for local file masking
To enhance the usability and performance of file masking, users can now pre-upload files to the server before starting the masking process. This feature supports local files, eliminating the need to upload files directly during masking and addressing challenges with large file handling.
Support for masking zipped files
Our service now includes support for masking zipped files. This feature automatically unpacks zip files during the masking process, allowing for efficient handling and processing of large zipped datasets.
Overview:
Appendix. The new features tickets details:
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Appendix. Important fixed bugs details:
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The list of new known issues found since the previous code freeze:
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The manual regression tests - completed
https://docs.google.com/spreadsheets/d/13qh7ZgjWAqsrkxOrGZy3nlKus32_lGpKV_LKAd1mgzM/edit?usp=sharing
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The automated smoke tests - completed
All supported versions have been tested and work as expected:
MongoDB v3.6/5/6
DB2 LUW v11.5
DB2 z/OS v11
Oracle v12cR2/19c RDS
MSSQL v2019/2016 RDS
PostgreSQL v13.2/14.3 RDS
MySQL v5.7/8 RDS
MySQL Aurora v5.7
SAP HANA v2.00.05
Fixed/CSV files
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